Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations39129
Missing cells6399
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory144.0 B

Variable types

Categorical4
Text4
Boolean1
Numeric9

Alerts

Rfrg_Prvdr_Geo_Lvl is highly imbalanced (74.8%)Imbalance
Rfrg_Prvdr_Geo_Cd has 1649 (4.2%) missing valuesMissing
Tot_Suplr_Benes has 4750 (12.1%) missing valuesMissing
Tot_Rfrg_Prvdrs is highly skewed (γ1 = 31.88771723)Skewed
Tot_Suplrs is highly skewed (γ1 = 42.08429491)Skewed
Tot_Suplr_Benes is highly skewed (γ1 = 48.54514462)Skewed
Tot_Suplr_Clms is highly skewed (γ1 = 54.4026195)Skewed
Tot_Suplr_Srvcs is highly skewed (γ1 = 162.2314046)Skewed

Reproduction

Analysis started2024-09-20 02:18:32.162567
Analysis finished2024-09-20 02:19:01.508439
Duration29.35 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Rfrg_Prvdr_Geo_Lvl
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 KiB
State
37480 
National
 
1649

Length

Max length8
Median length5
Mean length5.126428
Min length5

Characters and Unicode

Total characters200592
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational
2nd rowState
3rd rowState
4th rowState
5th rowState

Common Values

ValueCountFrequency (%)
State 37480
95.8%
National 1649
 
4.2%

Length

2024-09-20T02:19:01.748381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T02:19:02.289122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
state 37480
95.8%
national 1649
 
4.2%

Most occurring characters

ValueCountFrequency (%)
t 76609
38.2%
a 40778
20.3%
S 37480
18.7%
e 37480
18.7%
N 1649
 
0.8%
i 1649
 
0.8%
o 1649
 
0.8%
n 1649
 
0.8%
l 1649
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 76609
38.2%
a 40778
20.3%
S 37480
18.7%
e 37480
18.7%
N 1649
 
0.8%
i 1649
 
0.8%
o 1649
 
0.8%
n 1649
 
0.8%
l 1649
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 76609
38.2%
a 40778
20.3%
S 37480
18.7%
e 37480
18.7%
N 1649
 
0.8%
i 1649
 
0.8%
o 1649
 
0.8%
n 1649
 
0.8%
l 1649
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 76609
38.2%
a 40778
20.3%
S 37480
18.7%
e 37480
18.7%
N 1649
 
0.8%
i 1649
 
0.8%
o 1649
 
0.8%
n 1649
 
0.8%
l 1649
 
0.8%

Rfrg_Prvdr_Geo_Cd
Text

MISSING 

Distinct60
Distinct (%)0.2%
Missing1649
Missing (%)4.2%
Memory size305.8 KiB
2024-09-20T02:19:02.863131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters74960
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04
2nd row06
3rd row20
4th row26
5th row27
ValueCountFrequency (%)
06 1114
 
3.0%
48 1023
 
2.7%
36 1016
 
2.7%
12 1002
 
2.7%
42 960
 
2.6%
17 933
 
2.5%
39 910
 
2.4%
37 898
 
2.4%
26 870
 
2.3%
34 869
 
2.3%
Other values (50) 27885
74.4%
2024-09-20T02:19:03.883584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 11697
15.6%
1 10549
14.1%
4 10204
13.6%
3 9919
13.2%
0 8188
10.9%
5 7947
10.6%
6 4636
 
6.2%
9 3930
 
5.2%
8 3904
 
5.2%
7 3816
 
5.1%
Other values (5) 170
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 11697
15.6%
1 10549
14.1%
4 10204
13.6%
3 9919
13.2%
0 8188
10.9%
5 7947
10.6%
6 4636
 
6.2%
9 3930
 
5.2%
8 3904
 
5.2%
7 3816
 
5.1%
Other values (5) 170
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 11697
15.6%
1 10549
14.1%
4 10204
13.6%
3 9919
13.2%
0 8188
10.9%
5 7947
10.6%
6 4636
 
6.2%
9 3930
 
5.2%
8 3904
 
5.2%
7 3816
 
5.1%
Other values (5) 170
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 11697
15.6%
1 10549
14.1%
4 10204
13.6%
3 9919
13.2%
0 8188
10.9%
5 7947
10.6%
6 4636
 
6.2%
9 3930
 
5.2%
8 3904
 
5.2%
7 3816
 
5.1%
Other values (5) 170
 
0.2%
Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:04.343567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length34
Median length20
Mean length8.6586675
Min length4

Characters and Unicode

Total characters338805
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational
2nd rowArizona
3rd rowCalifornia
4th rowKansas
5th rowMichigan
ValueCountFrequency (%)
new 3024
 
6.4%
carolina 1663
 
3.5%
national 1649
 
3.5%
virginia 1454
 
3.1%
north 1366
 
2.9%
south 1295
 
2.7%
california 1114
 
2.3%
texas 1023
 
2.2%
york 1016
 
2.1%
florida 1002
 
2.1%
Other values (62) 32832
69.2%
2024-09-20T02:19:05.065392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 44691
13.2%
i 33733
 
10.0%
n 28872
 
8.5%
o 27497
 
8.1%
s 23695
 
7.0%
e 20894
 
6.2%
r 17284
 
5.1%
t 14278
 
4.2%
l 13300
 
3.9%
h 9047
 
2.7%
Other values (38) 105514
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 338805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 44691
13.2%
i 33733
 
10.0%
n 28872
 
8.5%
o 27497
 
8.1%
s 23695
 
7.0%
e 20894
 
6.2%
r 17284
 
5.1%
t 14278
 
4.2%
l 13300
 
3.9%
h 9047
 
2.7%
Other values (38) 105514
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 338805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 44691
13.2%
i 33733
 
10.0%
n 28872
 
8.5%
o 27497
 
8.1%
s 23695
 
7.0%
e 20894
 
6.2%
r 17284
 
5.1%
t 14278
 
4.2%
l 13300
 
3.9%
h 9047
 
2.7%
Other values (38) 105514
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 338805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 44691
13.2%
i 33733
 
10.0%
n 28872
 
8.5%
o 27497
 
8.1%
s 23695
 
7.0%
e 20894
 
6.2%
r 17284
 
5.1%
t 14278
 
4.2%
l 13300
 
3.9%
h 9047
 
2.7%
Other values (38) 105514
31.1%

RBCS_Lvl
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 KiB
Durable Medical Equipment
19724 
Orthotic Devices
14302 
Unknown
4305 
Drugs Administered Through DME
 
798

Length

Max length30
Median length25
Mean length19.832017
Min length7

Characters and Unicode

Total characters776007
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDrugs Administered Through DME
2nd rowDrugs Administered Through DME
3rd rowDrugs Administered Through DME
4th rowDrugs Administered Through DME
5th rowDrugs Administered Through DME

Common Values

ValueCountFrequency (%)
Durable Medical Equipment 19724
50.4%
Orthotic Devices 14302
36.6%
Unknown 4305
 
11.0%
Drugs Administered Through DME 798
 
2.0%

Length

2024-09-20T02:19:05.387674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T02:19:05.654576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
durable 19724
20.7%
medical 19724
20.7%
equipment 19724
20.7%
orthotic 14302
15.0%
devices 14302
15.0%
unknown 4305
 
4.5%
drugs 798
 
0.8%
administered 798
 
0.8%
through 798
 
0.8%
dme 798
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e 89372
 
11.5%
i 69648
 
9.0%
56144
 
7.2%
t 49126
 
6.3%
c 48328
 
6.2%
u 41044
 
5.3%
a 39448
 
5.1%
l 39448
 
5.1%
r 36420
 
4.7%
D 35622
 
4.6%
Other values (19) 271407
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 776007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 89372
 
11.5%
i 69648
 
9.0%
56144
 
7.2%
t 49126
 
6.3%
c 48328
 
6.2%
u 41044
 
5.3%
a 39448
 
5.1%
l 39448
 
5.1%
r 36420
 
4.7%
D 35622
 
4.6%
Other values (19) 271407
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 776007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 89372
 
11.5%
i 69648
 
9.0%
56144
 
7.2%
t 49126
 
6.3%
c 48328
 
6.2%
u 41044
 
5.3%
a 39448
 
5.1%
l 39448
 
5.1%
r 36420
 
4.7%
D 35622
 
4.6%
Other values (19) 271407
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 776007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 89372
 
11.5%
i 69648
 
9.0%
56144
 
7.2%
t 49126
 
6.3%
c 48328
 
6.2%
u 41044
 
5.3%
a 39448
 
5.1%
l 39448
 
5.1%
r 36420
 
4.7%
D 35622
 
4.6%
Other values (19) 271407
35.0%

RBCS_Id
Categorical

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size305.8 KiB
DF000N
7824 
DE000N
5275 
DD021N
4306 
DF010N
2897 
DA023N
2741 
Other values (30)
16086 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters234774
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDG000N
2nd rowDG000N
3rd rowDG000N
4th rowDG000N
5th rowDG000N

Common Values

ValueCountFrequency (%)
DF000N 7824
20.0%
DE000N 5275
13.5%
DD021N 4306
11.0%
DF010N 2897
 
7.4%
DA023N 2741
 
7.0%
DF003N 2352
 
6.0%
DD009N 2133
 
5.5%
DA000N 1176
 
3.0%
DE001N 1138
 
2.9%
OC000N 1062
 
2.7%
Other values (25) 8225
21.0%

Length

2024-09-20T02:19:05.926771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
df000n 7824
20.0%
de000n 5275
13.5%
dd021n 4306
11.0%
df010n 2897
 
7.4%
da023n 2741
 
7.0%
df003n 2352
 
6.0%
dd009n 2133
 
5.5%
da000n 1176
 
3.0%
de001n 1138
 
2.9%
oc000n 1062
 
2.7%
Other values (25) 8225
21.0%

Most occurring characters

ValueCountFrequency (%)
0 86755
37.0%
D 41959
17.9%
N 39129
16.7%
F 14302
 
6.1%
1 10444
 
4.4%
2 8445
 
3.6%
E 7262
 
3.1%
3 5144
 
2.2%
A 3917
 
1.7%
9 2843
 
1.2%
Other values (13) 14574
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 86755
37.0%
D 41959
17.9%
N 39129
16.7%
F 14302
 
6.1%
1 10444
 
4.4%
2 8445
 
3.6%
E 7262
 
3.1%
3 5144
 
2.2%
A 3917
 
1.7%
9 2843
 
1.2%
Other values (13) 14574
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 86755
37.0%
D 41959
17.9%
N 39129
16.7%
F 14302
 
6.1%
1 10444
 
4.4%
2 8445
 
3.6%
E 7262
 
3.1%
3 5144
 
2.2%
A 3917
 
1.7%
9 2843
 
1.2%
Other values (13) 14574
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 86755
37.0%
D 41959
17.9%
N 39129
16.7%
F 14302
 
6.1%
1 10444
 
4.4%
2 8445
 
3.6%
E 7262
 
3.1%
3 5144
 
2.2%
A 3917
 
1.7%
9 2843
 
1.2%
Other values (13) 14574
 
6.2%

RBCS_Desc
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 KiB
DME-Orthotic Devices
14302 
DME-Other DME
7262 
DME-Wheelchairs
7135 
DME-Medical/Surgical Supplies
3917 
Other-Enteral and Parenteral
 
1198
Other values (7)
5315 

Length

Max length49
Median length42
Mean length20.749291
Min length13

Characters and Unicode

Total characters811899
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDME-Drugs Administered Through DME
2nd rowDME-Drugs Administered Through DME
3rd rowDME-Drugs Administered Through DME
4th rowDME-Drugs Administered Through DME
5th rowDME-Drugs Administered Through DME

Common Values

ValueCountFrequency (%)
DME-Orthotic Devices 14302
36.6%
DME-Other DME 7262
18.6%
DME-Wheelchairs 7135
18.2%
DME-Medical/Surgical Supplies 3917
 
10.0%
Other-Enteral and Parenteral 1198
 
3.1%
Other-Vision, Hearing, and Speech Services 1062
 
2.7%
DME-Hospital Beds 837
 
2.1%
Treatment-Treatment - Miscellaneous 835
 
2.1%
Treatment-Injections and Infusions (nononcologic) 826
 
2.1%
DME-Drugs Administered Through DME 798
 
2.0%
Other values (2) 957
 
2.4%

Length

2024-09-20T02:19:06.199835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dme-orthotic 14302
17.9%
devices 14302
17.9%
dme 8060
10.1%
dme-other 7262
9.1%
dme-wheelchairs 7135
8.9%
supplies 4490
 
5.6%
dme-medical/surgical 3917
 
4.9%
and 3659
 
4.6%
other-enteral 1198
 
1.5%
parenteral 1198
 
1.5%
Other values (17) 14256
17.9%

Most occurring characters

ValueCountFrequency (%)
e 81737
 
10.1%
i 58057
 
7.2%
D 57984
 
7.1%
t 49127
 
6.1%
c 49010
 
6.0%
M 47636
 
5.9%
r 46252
 
5.7%
E 44082
 
5.4%
h 41520
 
5.1%
40650
 
5.0%
Other values (31) 295844
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 811899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 81737
 
10.1%
i 58057
 
7.2%
D 57984
 
7.1%
t 49127
 
6.1%
c 49010
 
6.0%
M 47636
 
5.9%
r 46252
 
5.7%
E 44082
 
5.4%
h 41520
 
5.1%
40650
 
5.0%
Other values (31) 295844
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 811899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 81737
 
10.1%
i 58057
 
7.2%
D 57984
 
7.1%
t 49127
 
6.1%
c 49010
 
6.0%
M 47636
 
5.9%
r 46252
 
5.7%
E 44082
 
5.4%
h 41520
 
5.1%
40650
 
5.0%
Other values (31) 295844
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 811899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 81737
 
10.1%
i 58057
 
7.2%
D 57984
 
7.1%
t 49127
 
6.1%
c 49010
 
6.0%
M 47636
 
5.9%
r 46252
 
5.7%
E 44082
 
5.4%
h 41520
 
5.1%
40650
 
5.0%
Other values (31) 295844
36.4%
Distinct1493
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:06.750801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters195645
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)0.6%

Sample

1st rowJ2545
2nd rowJ2545
3rd rowJ2545
4th rowJ2545
5th rowJ2545
ValueCountFrequency (%)
e0562 110
 
0.3%
e0143 110
 
0.3%
e1390 110
 
0.3%
e1028 106
 
0.3%
e0971 105
 
0.3%
e0955 104
 
0.3%
k0554 101
 
0.3%
e2201 100
 
0.3%
e0156 100
 
0.3%
e0973 97
 
0.2%
Other values (1483) 38086
97.3%
2024-09-20T02:19:07.584089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28860
14.8%
2 19960
10.2%
4 17020
8.7%
1 16725
8.5%
5 16560
8.5%
3 14620
7.5%
6 14532
7.4%
E 11210
 
5.7%
A 10859
 
5.6%
7 10684
 
5.5%
Other values (9) 34615
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 195645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28860
14.8%
2 19960
10.2%
4 17020
8.7%
1 16725
8.5%
5 16560
8.5%
3 14620
7.5%
6 14532
7.4%
E 11210
 
5.7%
A 10859
 
5.6%
7 10684
 
5.5%
Other values (9) 34615
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 195645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28860
14.8%
2 19960
10.2%
4 17020
8.7%
1 16725
8.5%
5 16560
8.5%
3 14620
7.5%
6 14532
7.4%
E 11210
 
5.7%
A 10859
 
5.6%
7 10684
 
5.5%
Other values (9) 34615
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 195645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28860
14.8%
2 19960
10.2%
4 17020
8.7%
1 16725
8.5%
5 16560
8.5%
3 14620
7.5%
6 14532
7.4%
E 11210
 
5.7%
A 10859
 
5.6%
7 10684
 
5.5%
Other values (9) 34615
17.7%
Distinct1493
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:08.163370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length597
Median length250
Mean length96.972246
Min length7

Characters and Unicode

Total characters3794427
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)0.6%

Sample

1st rowPentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mg
2nd rowPentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mg
3rd rowPentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mg
4th rowPentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mg
5th rowPentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mg
ValueCountFrequency (%)
or 14924
 
2.9%
with 14276
 
2.7%
each 11681
 
2.2%
to 9362
 
1.8%
and 8620
 
1.7%
wheelchair 6416
 
1.2%
any 6227
 
1.2%
for 5855
 
1.1%
without 4760
 
0.9%
than 4645
 
0.9%
Other values (1867) 434699
83.4%
2024-09-20T02:19:09.080611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
482336
 
12.7%
e 360210
 
9.5%
t 256375
 
6.8%
i 252565
 
6.7%
a 237833
 
6.3%
r 222692
 
5.9%
o 217311
 
5.7%
n 204645
 
5.4%
s 191484
 
5.0%
l 153819
 
4.1%
Other values (63) 1215157
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3794427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
482336
 
12.7%
e 360210
 
9.5%
t 256375
 
6.8%
i 252565
 
6.7%
a 237833
 
6.3%
r 222692
 
5.9%
o 217311
 
5.7%
n 204645
 
5.4%
s 191484
 
5.0%
l 153819
 
4.1%
Other values (63) 1215157
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3794427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
482336
 
12.7%
e 360210
 
9.5%
t 256375
 
6.8%
i 252565
 
6.7%
a 237833
 
6.3%
r 222692
 
5.9%
o 217311
 
5.7%
n 204645
 
5.4%
s 191484
 
5.0%
l 153819
 
4.1%
Other values (63) 1215157
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3794427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
482336
 
12.7%
e 360210
 
9.5%
t 256375
 
6.8%
i 252565
 
6.7%
a 237833
 
6.3%
r 222692
 
5.9%
o 217311
 
5.7%
n 204645
 
5.4%
s 191484
 
5.0%
l 153819
 
4.1%
Other values (63) 1215157
32.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
False
32540 
True
6589 
ValueCountFrequency (%)
False 32540
83.2%
True 6589
 
16.8%
2024-09-20T02:19:09.407245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Tot_Rfrg_Prvdrs
Real number (ℝ)

SKEWED 

Distinct2806
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.35066
Minimum1
Maximum224353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:09.649404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median45
Q3168
95-th percentile1293.2
Maximum224353
Range224352
Interquartile range (IQR)153

Descriptive statistics

Standard deviation3880.5023
Coefficient of variation (CV)8.7923337
Kurtosis1298.854
Mean441.35066
Median Absolute Deviation (MAD)37
Skewness31.887717
Sum17269610
Variance15058298
MonotonicityNot monotonic
2024-09-20T02:19:09.924796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 848
 
2.2%
10 747
 
1.9%
12 746
 
1.9%
3 714
 
1.8%
13 708
 
1.8%
4 700
 
1.8%
2 664
 
1.7%
9 658
 
1.7%
5 656
 
1.7%
6 652
 
1.7%
Other values (2796) 32036
81.9%
ValueCountFrequency (%)
1 397
1.0%
2 664
1.7%
3 714
1.8%
4 700
1.8%
5 656
1.7%
6 652
1.7%
7 642
1.6%
8 649
1.7%
9 658
1.7%
10 747
1.9%
ValueCountFrequency (%)
224353 1
< 0.1%
218972 1
< 0.1%
186881 1
< 0.1%
171034 1
< 0.1%
164821 1
< 0.1%
158277 1
< 0.1%
155608 1
< 0.1%
138569 1
< 0.1%
129140 1
< 0.1%
128284 1
< 0.1%

Tot_Suplrs
Real number (ℝ)

SKEWED 

Distinct1296
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.616295
Minimum1
Maximum42955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:10.213653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median17
Q346
95-th percentile285
Maximum42955
Range42954
Interquartile range (IQR)39

Descriptive statistics

Standard deviation635.6302
Coefficient of variation (CV)7.1728366
Kurtosis2312.1653
Mean88.616295
Median Absolute Deviation (MAD)12
Skewness42.084295
Sum3467467
Variance404025.75
MonotonicityNot monotonic
2024-09-20T02:19:10.492253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1611
 
4.1%
6 1530
 
3.9%
7 1490
 
3.8%
3 1482
 
3.8%
5 1478
 
3.8%
4 1470
 
3.8%
8 1382
 
3.5%
9 1284
 
3.3%
1 1182
 
3.0%
10 1182
 
3.0%
Other values (1286) 25038
64.0%
ValueCountFrequency (%)
1 1182
3.0%
2 1611
4.1%
3 1482
3.8%
4 1470
3.8%
5 1478
3.8%
6 1530
3.9%
7 1490
3.8%
8 1382
3.5%
9 1284
3.3%
10 1182
3.0%
ValueCountFrequency (%)
42955 1
< 0.1%
42068 1
< 0.1%
39640 1
< 0.1%
36566 1
< 0.1%
33519 1
< 0.1%
33444 1
< 0.1%
26287 1
< 0.1%
23872 1
< 0.1%
21439 1
< 0.1%
19686 1
< 0.1%

Tot_Suplr_Benes
Real number (ℝ)

MISSING  SKEWED 

Distinct4231
Distinct (%)12.3%
Missing4750
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean1723.1855
Minimum11
Maximum1758956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:10.772093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile13
Q130
median89
Q3355
95-th percentile3992.1
Maximum1758956
Range1758945
Interquartile range (IQR)325

Descriptive statistics

Standard deviation23864.228
Coefficient of variation (CV)13.848902
Kurtosis2867.0691
Mean1723.1855
Median Absolute Deviation (MAD)72
Skewness48.545145
Sum59241394
Variance5.6950137 × 108
MonotonicityNot monotonic
2024-09-20T02:19:11.095887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 762
 
1.9%
12 728
 
1.9%
14 617
 
1.6%
15 604
 
1.5%
13 597
 
1.5%
18 477
 
1.2%
17 476
 
1.2%
16 457
 
1.2%
20 430
 
1.1%
19 426
 
1.1%
Other values (4221) 28805
73.6%
(Missing) 4750
 
12.1%
ValueCountFrequency (%)
11 762
1.9%
12 728
1.9%
13 597
1.5%
14 617
1.6%
15 604
1.5%
16 457
1.2%
17 476
1.2%
18 477
1.2%
19 426
1.1%
20 430
1.1%
ValueCountFrequency (%)
1758956 1
< 0.1%
1730195 1
< 0.1%
1572203 1
< 0.1%
1147174 1
< 0.1%
1120889 1
< 0.1%
1043882 1
< 0.1%
914369 1
< 0.1%
845299 1
< 0.1%
812338 1
< 0.1%
762685 1
< 0.1%

Tot_Suplr_Clms
Real number (ℝ)

SKEWED 

Distinct6057
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4161.6264
Minimum11
Maximum6264806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:11.842727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile13
Q134
median123
Q3586
95-th percentile8618
Maximum6264806
Range6264795
Interquartile range (IQR)552

Descriptive statistics

Standard deviation65725.124
Coefficient of variation (CV)15.793134
Kurtosis3753.8268
Mean4161.6264
Median Absolute Deviation (MAD)105
Skewness54.40262
Sum1.6284028 × 108
Variance4.3197919 × 109
MonotonicityNot monotonic
2024-09-20T02:19:12.156955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 790
 
2.0%
12 787
 
2.0%
13 622
 
1.6%
15 553
 
1.4%
14 553
 
1.4%
17 486
 
1.2%
16 474
 
1.2%
18 468
 
1.2%
19 442
 
1.1%
20 414
 
1.1%
Other values (6047) 33540
85.7%
ValueCountFrequency (%)
11 790
2.0%
12 787
2.0%
13 622
1.6%
14 553
1.4%
15 553
1.4%
16 474
1.2%
17 486
1.2%
18 468
1.2%
19 442
1.1%
20 414
1.1%
ValueCountFrequency (%)
6264806 1
< 0.1%
4135238 1
< 0.1%
4016438 1
< 0.1%
4010126 1
< 0.1%
3528456 1
< 0.1%
2861294 1
< 0.1%
2507590 1
< 0.1%
2439356 1
< 0.1%
2276199 1
< 0.1%
1885927 1
< 0.1%

Tot_Suplr_Srvcs
Real number (ℝ)

SKEWED 

Distinct11609
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114133.74
Minimum11
Maximum8.3911761 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:12.475436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile16
Q158
median324
Q33399
95-th percentile108544.8
Maximum8.3911761 × 108
Range8.391176 × 108
Interquartile range (IQR)3341

Descriptive statistics

Standard deviation4553071.8
Coefficient of variation (CV)39.892427
Kurtosis29523.064
Mean114133.74
Median Absolute Deviation (MAD)304
Skewness162.2314
Sum4.4659391 × 109
Variance2.0730463 × 1013
MonotonicityNot monotonic
2024-09-20T02:19:12.772504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 438
 
1.1%
11 401
 
1.0%
13 353
 
0.9%
15 350
 
0.9%
14 330
 
0.8%
16 327
 
0.8%
22 300
 
0.8%
17 297
 
0.8%
18 294
 
0.8%
20 289
 
0.7%
Other values (11599) 35750
91.4%
ValueCountFrequency (%)
11 401
1.0%
12 438
1.1%
13 353
0.9%
14 330
0.8%
15 350
0.9%
16 327
0.8%
17 297
0.8%
18 294
0.8%
19 288
0.7%
20 289
0.7%
ValueCountFrequency (%)
839117610 1
< 0.1%
149378130 1
< 0.1%
114684657 1
< 0.1%
86276057 1
< 0.1%
75047983 1
< 0.1%
73429961 1
< 0.1%
71546089 1
< 0.1%
68863150 1
< 0.1%
63824166 1
< 0.1%
58213431 1
< 0.1%

Avg_Suplr_Sbmtd_Chrg
Real number (ℝ)

Distinct38539
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean666.88575
Minimum0.06400364
Maximum77918.591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:13.060996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.06400364
5-th percentile2.5982014
Q118.985804
median99.571111
Q3460.42434
95-th percentile3000.8415
Maximum77918.591
Range77918.527
Interquartile range (IQR)441.43854

Descriptive statistics

Standard deviation2231.946
Coefficient of variation (CV)3.3468191
Kurtosis128.73268
Mean666.88575
Median Absolute Deviation (MAD)92.839809
Skewness9.211006
Sum26094572
Variance4981582.8
MonotonicityNot monotonic
2024-09-20T02:19:13.397476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
435 71
 
0.2%
50 48
 
0.1%
21000 42
 
0.1%
23.34 25
 
0.1%
14.75 24
 
0.1%
450 14
 
< 0.1%
407 13
 
< 0.1%
232.35 13
 
< 0.1%
4950 12
 
< 0.1%
461 12
 
< 0.1%
Other values (38529) 38855
99.3%
ValueCountFrequency (%)
0.0640036397 1
< 0.1%
0.0661930864 1
< 0.1%
0.0662181976 1
< 0.1%
0.067036943 1
< 0.1%
0.0684243106 1
< 0.1%
0.0709463096 1
< 0.1%
0.0720512821 1
< 0.1%
0.075873164 1
< 0.1%
0.0759708056 1
< 0.1%
0.076405182 1
< 0.1%
ValueCountFrequency (%)
77918.59067 1
< 0.1%
47185.57429 1
< 0.1%
44042.046 1
< 0.1%
39962.05176 1
< 0.1%
39916.72435 1
< 0.1%
39600.68906 1
< 0.1%
38062.22393 1
< 0.1%
36976.36517 1
< 0.1%
36241.50029 1
< 0.1%
35047.91526 1
< 0.1%

Avg_Suplr_Mdcr_Alowd_Amt
Real number (ℝ)

Distinct36272
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean386.54716
Minimum0.0125
Maximum39385.318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:13.718165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0125
5-th percentile0.79819074
Q19.0043497
median49.040323
Q3231.1426
95-th percentile1650.37
Maximum39385.318
Range39385.305
Interquartile range (IQR)222.13825

Descriptive statistics

Standard deviation1386.8901
Coefficient of variation (CV)3.5878936
Kurtosis165.11961
Mean386.54716
Median Absolute Deviation (MAD)45.867236
Skewness10.765898
Sum15125204
Variance1923464.1
MonotonicityNot monotonic
2024-09-20T02:19:14.048616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.14 49
 
0.1%
50 46
 
0.1%
3955.48 43
 
0.1%
3.51 43
 
0.1%
14065.72 42
 
0.1%
0.84 42
 
0.1%
0.14 38
 
0.1%
24 38
 
0.1%
16 37
 
0.1%
57 37
 
0.1%
Other values (36262) 38714
98.9%
ValueCountFrequency (%)
0.0125 1
< 0.1%
0.0125093284 1
< 0.1%
0.0125986842 1
< 0.1%
0.0127788462 1
< 0.1%
0.0129214781 1
< 0.1%
0.0130038388 1
< 0.1%
0.0130498972 1
< 0.1%
0.0130584016 1
< 0.1%
0.0131399317 1
< 0.1%
0.0131410256 1
< 0.1%
ValueCountFrequency (%)
39385.31786 1
< 0.1%
36264.92933 1
< 0.1%
25581.97125 1
< 0.1%
25513.495 1
< 0.1%
25504.24253 1
< 0.1%
25500.6716 1
< 0.1%
25498.56779 1
< 0.1%
25495.24692 1
< 0.1%
25482.63209 1
< 0.1%
25481.88288 1
< 0.1%

Avg_Suplr_Mdcr_Pymt_Amt
Real number (ℝ)

Distinct39090
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.56618
Minimum0
Maximum31179.861
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:14.553617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.61152189
Q16.8327273
median36.88726
Q3177.95812
95-th percentile1290.8854
Maximum31179.861
Range31179.861
Interquartile range (IQR)171.12539

Descriptive statistics

Standard deviation1082.0064
Coefficient of variation (CV)3.5998941
Kurtosis164.15789
Mean300.56618
Median Absolute Deviation (MAD)34.505508
Skewness10.728529
Sum11760854
Variance1170737.9
MonotonicityNot monotonic
2024-09-20T02:19:15.034379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.43 7
 
< 0.1%
0.22 4
 
< 0.1%
80.88666667 3
 
< 0.1%
1.36 3
 
< 0.1%
106.0875 3
 
< 0.1%
0.1276690307 2
 
< 0.1%
0.5315384615 2
 
< 0.1%
453.8883163 2
 
< 0.1%
4.76862069 2
 
< 0.1%
10.11 2
 
< 0.1%
Other values (39080) 39099
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.0091935484 1
< 0.1%
0.009636194 1
< 0.1%
0.0099337748 1
< 0.1%
0.01 1
< 0.1%
0.0100352564 1
< 0.1%
0.0100803588 1
< 0.1%
0.0100986842 1
< 0.1%
0.0102236538 1
< 0.1%
0.0102564103 1
< 0.1%
ValueCountFrequency (%)
31179.86071 1
< 0.1%
28625.18533 1
< 0.1%
20235.71938 1
< 0.1%
20064.51983 1
< 0.1%
20004.80235 1
< 0.1%
19977.88031 1
< 0.1%
19930.75922 1
< 0.1%
19929.77231 1
< 0.1%
19905.55441 1
< 0.1%
19872.86077 1
< 0.1%

Avg_Suplr_Mdcr_Stdzd_Amt
Real number (ℝ)

Distinct38083
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.82821
Minimum0.0091935484
Maximum29894.176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.8 KiB
2024-09-20T02:19:15.454511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0091935484
5-th percentile0.61968014
Q16.9133253
median37.376304
Q3178.30452
95-th percentile1280.4616
Maximum29894.176
Range29894.167
Interquartile range (IQR)171.3912

Descriptive statistics

Standard deviation1074.84
Coefficient of variation (CV)3.5968493
Kurtosis162.09223
Mean298.82821
Median Absolute Deviation (MAD)34.870124
Skewness10.682358
Sum11692849
Variance1155281.1
MonotonicityNot monotonic
2024-09-20T02:19:15.910786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.02 26
 
0.1%
1475.06 26
 
0.1%
284.56 23
 
0.1%
423.84 20
 
0.1%
397.83 17
 
< 0.1%
5085 16
 
< 0.1%
307.52 15
 
< 0.1%
80.34 15
 
< 0.1%
248.43 14
 
< 0.1%
368.87 14
 
< 0.1%
Other values (38073) 38943
99.5%
ValueCountFrequency (%)
0.0091935484 1
< 0.1%
0.0095988806 1
< 0.1%
0.0098573612 1
< 0.1%
0.01 1
< 0.1%
0.0100096154 1
< 0.1%
0.0100635395 1
< 0.1%
0.0100986842 1
< 0.1%
0.0101294518 1
< 0.1%
0.0101931382 1
< 0.1%
0.0102389078 1
< 0.1%
ValueCountFrequency (%)
29894.17643 1
< 0.1%
28426.59133 1
< 0.1%
19775.92 1
< 0.1%
19775.91961 1
< 0.1%
19775.91929 1
< 0.1%
19775.91017 1
< 0.1%
19775.91 2
< 0.1%
19774.515 1
< 0.1%
19772.91632 1
< 0.1%
19770.21156 1
< 0.1%

Interactions

2024-09-20T02:18:56.689995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:34.764703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:37.997579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:40.303535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:43.086754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:45.497060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:48.226597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:51.688385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:54.197407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:56.931442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:35.318361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:38.228942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:40.584014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:43.333946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:45.733045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:48.564854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:51.937719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:54.474415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:57.171557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:35.678498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:38.464044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:40.883050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:43.580429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:45.956972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:49.318614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:52.184944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:54.749802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:57.440904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:36.045500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:38.739985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:41.163060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:43.868748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:46.220916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:49.793002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:52.483279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:55.073740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:57.715313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:36.483299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:38.992678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:41.673287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:44.151555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:46.486954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:50.269790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:52.761889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:55.345308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:58.320105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:36.884613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:39.213497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:41.954363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:44.413573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:46.724054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:50.554192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:53.017826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:55.628399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:58.620180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:37.177223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:39.492593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:42.248688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:44.681223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:47.043628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:50.851107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:53.306420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:55.910792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:58.900839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:37.462159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:39.775896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:42.549218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:44.962979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:47.501944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:51.142579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:53.634777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:56.168215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:59.175035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:37.723153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:40.040024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:42.819267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:45.232258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:47.880950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:51.423776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:53.922411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-20T02:18:56.424254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-09-20T02:18:59.631518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-20T02:19:00.402539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-20T02:19:01.207144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Rfrg_Prvdr_Geo_LvlRfrg_Prvdr_Geo_CdRfrg_Prvdr_Geo_DescRBCS_LvlRBCS_IdRBCS_DescHCPCS_CdHCPCS_DescSuplr_Rentl_IndTot_Rfrg_PrvdrsTot_SuplrsTot_Suplr_BenesTot_Suplr_ClmsTot_Suplr_SrvcsAvg_Suplr_Sbmtd_ChrgAvg_Suplr_Mdcr_Alowd_AmtAvg_Suplr_Mdcr_Pymt_AmtAvg_Suplr_Mdcr_Stdzd_Amt
0NationalNaNNationalDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN573494.0519535336.673682117.51768289.81418789.074000
1State04ArizonaDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN23NaN2222188.371818119.64636494.48681893.794545
2State06CaliforniaDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN23NaN3535435.754286107.95057183.16057182.598000
3State20KansasDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN8218.05656216.937321121.82910795.12214394.192500
4State26MichiganDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN84NaN6365250.409077118.70138592.17984691.542154
5State27MinnesotaDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN11NaN1212520.700000119.49000094.43250093.670000
6State37North CarolinaDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN45NaN4446181.256304101.59282675.38978374.683478
7State39OhioDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN45NaN3636394.853889120.64972292.71888991.845278
8State42PennsylvaniaDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN62NaN1717185.671765123.07470691.70294190.686471
9State48TexasDrugs Administered Through DMEDG000NDME-Drugs Administered Through DMEJ2545Pentamidine isethionate, inhalation solution, fda-approved final product, non-compounded, administered through dme, unit dose form, per 300 mgN8321.0179179484.183128119.52938590.07229189.413520
Rfrg_Prvdr_Geo_LvlRfrg_Prvdr_Geo_CdRfrg_Prvdr_Geo_DescRBCS_LvlRBCS_IdRBCS_DescHCPCS_CdHCPCS_DescSuplr_Rentl_IndTot_Rfrg_PrvdrsTot_SuplrsTot_Suplr_BenesTot_Suplr_ClmsTot_Suplr_SrvcsAvg_Suplr_Sbmtd_ChrgAvg_Suplr_Mdcr_Alowd_AmtAvg_Suplr_Mdcr_Pymt_AmtAvg_Suplr_Mdcr_Stdzd_Amt
39119State37North CarolinaUnknownRX029NTreatment-Treatment - MiscellaneousJ8705Topotecan, oral, 0.25 mgN74NaN271296116.158457104.32385082.20405181.732137
39120State45South CarolinaUnknownRX029NTreatment-Treatment - MiscellaneousJ8705Topotecan, oral, 0.25 mgN21NaN131560144.790667104.30538582.49007181.643968
39121State48TexasUnknownRX029NTreatment-Treatment - MiscellaneousJ8705Topotecan, oral, 0.25 mgN54NaN12680145.655735104.28826582.74683881.639618
39122State55WisconsinUnknownRX029NTreatment-Treatment - MiscellaneousJ8705Topotecan, oral, 0.25 mgN98NaN321795139.155014104.31542182.27895381.674652
39123NationalNaNNationalUnknownRX029NTreatment-Treatment - MiscellaneousJ8999Prescription drug, oral, chemotherapeutic, nosN927141.049744284114.9516791.5349791.2065411.194950
39124State06CaliforniaUnknownRX029NTreatment-Treatment - MiscellaneousJ8999Prescription drug, oral, chemotherapeutic, nosN12215.0584289129.6986551.0448660.7726490.767321
39125State16IdahoUnknownRX029NTreatment-Treatment - MiscellaneousJ8999Prescription drug, oral, chemotherapeutic, nosN51NaN29273844.4177361.0120530.7939630.793477
39126State34New JerseyUnknownRX029NTreatment-Treatment - MiscellaneousJ8999Prescription drug, oral, chemotherapeutic, nosN31NaN3126182.3262031.0049620.7897670.787907
39127State36New YorkUnknownRX029NTreatment-Treatment - MiscellaneousJ8999Prescription drug, oral, chemotherapeutic, nosN21NaN19120958.8067821.2362530.9699010.969297
39128State42PennsylvaniaUnknownRX029NTreatment-Treatment - MiscellaneousJ8999Prescription drug, oral, chemotherapeutic, nosN53187.031128758140.9082211.8225211.4415911.425465